Cutover windows close hard before peak season. A blocked deployment puts the entire retail calendar at risk.

The orchestrator runs iteratively. Failed objects retry across passes as dependencies converge. Phases that fail surface a typed error and route to a concrete fix path. No silent stalls.

Target databases are split across cloud-managed PostgreSQL services with subtle dialect and extension differences.

Trekport targets PostgreSQL 14, 15, 16, 17, and 18 across the major managed services. The compatibility layer ships the extensions that converted code depends on. Target-specific tuning lives in configuration, not in the conversion output.

Order-management and inventory systems generate billions of rows. Data transfer cannot become the bottleneck.

Data transfer uses parallel extraction with batched, schema-aware bulk load. Temporal and binary columns route to the correct loader path automatically. Row counts and checksums are validated against the source before the migration is signed off.

Black Friday and quarter-end peaks demand that the migrated PostgreSQL target hold up under read traffic two orders of magnitude above baseline within hours of cutover.

Trekport translates Oracle hint behavior to PostgreSQL extended statistics, materialized-view refresh schedules, and partition pruning where the source relied on Oracle features that PostgreSQL handles differently. The cutover scorecard flags any object whose performance characteristics may shift on the new target, so the database team enters peak with a known surface, not a surprise.

Retail platforms evolve weekly. The Oracle source keeps changing during the migration program; the converted target cannot drift from the live source.

Run Trekport in CI. The same conversion produces the same output, so a fresh extraction and conversion against the current Oracle source can be diffed against the previous run. Drift between source and target is detected at the schema-object level and routed to a typed delta report, not buried in a row-count summary.